Automatic Recognition of Offensive Team Formation in American Football Plays

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"Automatic Recognition of Offensive Team Formation in American Football Plays"

Bibliography:

I. Atmosukarto, B. Ghanem, S. Ahuja, K. Muthuswamy​, N.Ahuja

"Automatic Recognition of Offensive Team Formation in American Football Plays"

IEEE International Workshop on Computer Vision in Sports (CVsports), 2013​​

Publication Extra Information:

​Won Best Paper Award​

Authors:

I. Atmosukarto, Bernard Ghanem, S. Ahuja, K. Muthuswamy, N. Ahuja

Keywords:

Automatic Recognition, American Football Plays

Year:

2013

Abstract:

Abstract

​Compared to security surveillance and military applications, where automated action analysis is prevalent, the
sports domain is extremely under-served. Most existing
software packages for sports video analysis require manual annotation of important events in the video. American football is the most popular sport in the United States,
however most game analysis is still done manually. Line
of scrimmage and offensive team formation recognition are
two statistics that must be tagged by American Football
coaches when watching and evaluating past play video
clips, a process which takes many man hours per week.
These two statistics are also the building blocks for more
high-level analysis such as play strategy inference and automatic statistic generation. In this paper, we propose a novel
framework where given an American football play clip, we
automatically identify the video frame in which the offensive
team lines in formation (formation frame), the line of scrimmage for that play, and the type of player formation the offensive team takes on. The proposed framework achieves
95% accuracy in detecting the formation frame, 98% accuracy in detecting the line of scrimmage, and up to 67% accuracy in classifying the offensive team’s formation. To validate our framework, we compiled a large dataset comprising more than 800 play-clips of standard and high definition
resolution from real-world football games. This dataset will
be made publicly available for future comparison.​

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